Results (PhD Chapter 2)
Section 2/2
This series of files compile all analyses done during Chapter 2:
- Section 1 presents indices of influence calculations.
- Section 2 presents HMSC and regressions results.
All analyses have been done with R 3.6.2.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
To assess Section 1, click here.
To go back to the summary page, click here.
Human activities considered for the analyses:
- city influence: CityInf
- industries influence: InduInf
- dredging collecting zones: DredColl
- dredging dumping zones: DredDump
- commercial ships mooring site: MoorSite
- rainwater sewers: RainSew
- wastewater sewers: WastSew
- city wharves: CityWha
- industries wharves: InduWha
- fisheries (gear used):
- traps: FishTrap
- bottom-trawling: FishTraw
- longline: FishLine
- nets: FishNet
- dredge: FishDred
Data is also available for the number of captured individuals for dogwhelk (Buccinum sp.), common crab (Cancer irroratus), snowcrab (Chinoecetes opilio), nordic shrimp (Pandalus borealis), arctic surfclam (Mactromeris polynyma) and american lobster (Homarus americanus) fisheries.
1. Explorations
1.1. Relationships between parameters
This section explores relationships between each pair of parameters or AH distances.
Fist, we can compute the Spearman’s correlation between each parameter.
| Â | om | gravel | sand | silt | clay | arsenic | cadmium | chromium | copper | iron | manganese | mercury | lead | zinc | S | N | H | J | city | dredging_collect | dredging_dump | industry | mooring | sewers_rain | sewers_waste | wharves_city | wharves_industry | CI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| om | 1 | -0.379 | -0.708 | 0.68 | 0.023 | 0.423 | 0.409 | 0.399 | 0.388 | 0.455 | 0.514 | 0.438 | 0.422 | 0.369 | -0.022 | -0.121 | 0.124 | 0.136 | -0.005 | 0.378 | 0.335 | 0.236 | 0.514 | 0.233 | 0.396 | -0.052 | 0.354 | 0.365 |
| gravel | -0.379 | 1 | 0.186 | -0.471 | 0.099 | -0.077 | -0.133 | -0.102 | -0.127 | -0.148 | -0.153 | -0.184 | -0.093 | -0.113 | 0.032 | 0.007 | 0.013 | 0.07 | -0.071 | -0.15 | -0.134 | -0.18 | -0.218 | 0.026 | -0.052 | -0.064 | -0.163 | -0.131 |
| sand | -0.708 | 0.186 | 1 | -0.788 | -0.308 | -0.577 | -0.5 | -0.488 | -0.428 | -0.372 | -0.5 | -0.623 | -0.576 | -0.466 | 0.053 | 0.083 | 0.026 | -0.026 | 0.251 | -0.145 | -0.147 | 0.006 | -0.421 | -0.301 | -0.473 | 0.301 | -0.152 | -0.164 |
| silt | 0.68 | -0.471 | -0.788 | 1 | 0.05 | 0.455 | 0.453 | 0.381 | 0.345 | 0.286 | 0.413 | 0.557 | 0.474 | 0.374 | -0.044 | -0.011 | -0.062 | -0.057 | -0.134 | 0.16 | 0.139 | 0.029 | 0.424 | 0.228 | 0.388 | -0.18 | 0.156 | 0.169 |
| clay | 0.023 | 0.099 | -0.308 | 0.05 | 1 | 0.18 | 0.15 | 0.177 | 0.103 | 0.093 | 0.165 | 0.054 | 0.139 | 0.123 | -0.101 | -0.076 | -0.042 | 0.021 | -0.021 | 0.033 | 0.009 | 0.01 | 0.007 | 0.246 | 0.244 | -0.03 | 0.037 | 0.097 |
| arsenic | 0.423 | -0.077 | -0.577 | 0.455 | 0.18 | 1 | 0.736 | 0.833 | 0.814 | 0.775 | 0.85 | 0.679 | 0.9 | 0.829 | -0.195 | -0.002 | -0.274 | -0.186 | -0.149 | 0.396 | 0.186 | 0.218 | 0.66 | 0.627 | 0.745 | -0.193 | 0.385 | 0.45 |
| cadmium | 0.409 | -0.133 | -0.5 | 0.453 | 0.15 | 0.736 | 1 | 0.816 | 0.71 | 0.583 | 0.746 | 0.689 | 0.846 | 0.822 | -0.339 | -0.073 | -0.361 | -0.154 | 0.044 | 0.177 | -0.157 | -0.012 | 0.565 | 0.575 | 0.727 | -0.094 | 0.101 | 0.294 |
| chromium | 0.399 | -0.102 | -0.488 | 0.381 | 0.177 | 0.833 | 0.816 | 1 | 0.908 | 0.818 | 0.919 | 0.724 | 0.907 | 0.935 | -0.31 | -0.03 | -0.381 | -0.221 | 0.035 | 0.424 | 0.011 | 0.222 | 0.637 | 0.668 | 0.789 | -0.048 | 0.343 | 0.487 |
| copper | 0.388 | -0.127 | -0.428 | 0.345 | 0.103 | 0.814 | 0.71 | 0.908 | 1 | 0.85 | 0.843 | 0.639 | 0.895 | 0.963 | -0.262 | -0.01 | -0.344 | -0.241 | 0.195 | 0.598 | 0.175 | 0.409 | 0.661 | 0.654 | 0.728 | 0.151 | 0.516 | 0.643 |
| iron | 0.455 | -0.148 | -0.372 | 0.286 | 0.093 | 0.775 | 0.583 | 0.818 | 0.85 | 1 | 0.87 | 0.462 | 0.729 | 0.808 | -0.301 | -0.144 | -0.279 | -0.088 | 0.133 | 0.651 | 0.28 | 0.488 | 0.586 | 0.594 | 0.636 | 0.097 | 0.608 | 0.664 |
| manganese | 0.514 | -0.153 | -0.5 | 0.413 | 0.165 | 0.85 | 0.746 | 0.919 | 0.843 | 0.87 | 1 | 0.606 | 0.829 | 0.831 | -0.307 | -0.112 | -0.287 | -0.091 | 0.047 | 0.559 | 0.246 | 0.391 | 0.732 | 0.698 | 0.824 | -0.022 | 0.516 | 0.611 |
| mercury | 0.438 | -0.184 | -0.623 | 0.557 | 0.054 | 0.679 | 0.689 | 0.724 | 0.639 | 0.462 | 0.606 | 1 | 0.805 | 0.706 | -0.156 | 0.062 | -0.264 | -0.234 | -0.241 | 0.172 | -0.072 | -0.031 | 0.52 | 0.395 | 0.594 | -0.309 | 0.122 | 0.18 |
| lead | 0.422 | -0.093 | -0.576 | 0.474 | 0.139 | 0.9 | 0.846 | 0.907 | 0.895 | 0.729 | 0.829 | 0.805 | 1 | 0.93 | -0.258 | -0.005 | -0.348 | -0.239 | -0.034 | 0.422 | 0.111 | 0.223 | 0.658 | 0.681 | 0.813 | -0.108 | 0.371 | 0.483 |
| zinc | 0.369 | -0.113 | -0.466 | 0.374 | 0.123 | 0.829 | 0.822 | 0.935 | 0.963 | 0.808 | 0.831 | 0.706 | 0.93 | 1 | -0.276 | -0.003 | -0.359 | -0.244 | 0.14 | 0.462 | 0.026 | 0.252 | 0.641 | 0.659 | 0.758 | 0.067 | 0.369 | 0.532 |
| S | -0.022 | 0.032 | 0.053 | -0.044 | -0.101 | -0.195 | -0.339 | -0.31 | -0.262 | -0.301 | -0.307 | -0.156 | -0.258 | -0.276 | 1 | 0.559 | 0.704 | -0.05 | -0.149 | -0.196 | 0.035 | -0.105 | -0.253 | -0.305 | -0.329 | -0.071 | -0.14 | -0.241 |
| N | -0.121 | 0.007 | 0.083 | -0.011 | -0.076 | -0.002 | -0.073 | -0.03 | -0.01 | -0.144 | -0.112 | 0.062 | -0.005 | -0.003 | 0.559 | 1 | -0.046 | -0.684 | -0.066 | -0.14 | -0.143 | -0.15 | -0.021 | -0.107 | -0.067 | -0.059 | -0.177 | -0.142 |
| H | 0.124 | 0.013 | 0.026 | -0.062 | -0.042 | -0.274 | -0.361 | -0.381 | -0.344 | -0.279 | -0.287 | -0.264 | -0.348 | -0.359 | 0.704 | -0.046 | 1 | 0.599 | -0.093 | -0.074 | 0.211 | 0.052 | -0.245 | -0.302 | -0.333 | -0.009 | 0.008 | -0.161 |
| J | 0.136 | 0.07 | -0.026 | -0.057 | 0.021 | -0.186 | -0.154 | -0.221 | -0.241 | -0.088 | -0.091 | -0.234 | -0.239 | -0.244 | -0.05 | -0.684 | 0.599 | 1 | 0.012 | 0.01 | 0.167 | 0.103 | -0.131 | -0.105 | -0.148 | 0.029 | 0.079 | -0.033 |
| city | -0.005 | -0.071 | 0.251 | -0.134 | -0.021 | -0.149 | 0.044 | 0.035 | 0.195 | 0.133 | 0.047 | -0.241 | -0.034 | 0.14 | -0.149 | -0.066 | -0.093 | 0.012 | 1 | 0.414 | 0.107 | 0.253 | 0.324 | 0.329 | 0.18 | 0.969 | 0.254 | 0.563 |
| dredging_collect | 0.378 | -0.15 | -0.145 | 0.16 | 0.033 | 0.396 | 0.177 | 0.424 | 0.598 | 0.651 | 0.559 | 0.172 | 0.422 | 0.462 | -0.196 | -0.14 | -0.074 | 0.01 | 0.414 | 1 | 0.743 | 0.895 | 0.589 | 0.585 | 0.554 | 0.467 | 0.95 | 0.942 |
| dredging_dump | 0.335 | -0.134 | -0.147 | 0.139 | 0.009 | 0.186 | -0.157 | 0.011 | 0.175 | 0.28 | 0.246 | -0.072 | 0.111 | 0.026 | 0.035 | -0.143 | 0.211 | 0.167 | 0.107 | 0.743 | 1 | 0.791 | 0.351 | 0.267 | 0.268 | 0.213 | 0.824 | 0.642 |
| industry | 0.236 | -0.18 | 0.006 | 0.029 | 0.01 | 0.218 | -0.012 | 0.222 | 0.409 | 0.488 | 0.391 | -0.031 | 0.223 | 0.252 | -0.105 | -0.15 | 0.052 | 0.103 | 0.253 | 0.895 | 0.791 | 1 | 0.31 | 0.431 | 0.349 | 0.346 | 0.946 | 0.79 |
| mooring | 0.514 | -0.218 | -0.421 | 0.424 | 0.007 | 0.66 | 0.565 | 0.637 | 0.661 | 0.586 | 0.732 | 0.52 | 0.658 | 0.641 | -0.253 | -0.021 | -0.245 | -0.131 | 0.324 | 0.589 | 0.351 | 0.31 | 1 | 0.581 | 0.76 | 0.265 | 0.457 | 0.679 |
| sewers_rain | 0.233 | 0.026 | -0.301 | 0.228 | 0.246 | 0.627 | 0.575 | 0.668 | 0.654 | 0.594 | 0.698 | 0.395 | 0.681 | 0.659 | -0.305 | -0.107 | -0.302 | -0.105 | 0.329 | 0.585 | 0.267 | 0.431 | 0.581 | 1 | 0.893 | 0.284 | 0.551 | 0.722 |
| sewers_waste | 0.396 | -0.052 | -0.473 | 0.388 | 0.244 | 0.745 | 0.727 | 0.789 | 0.728 | 0.636 | 0.824 | 0.594 | 0.813 | 0.758 | -0.329 | -0.067 | -0.333 | -0.148 | 0.18 | 0.554 | 0.268 | 0.349 | 0.76 | 0.893 | 1 | 0.087 | 0.482 | 0.659 |
| wharves_city | -0.052 | -0.064 | 0.301 | -0.18 | -0.03 | -0.193 | -0.094 | -0.048 | 0.151 | 0.097 | -0.022 | -0.309 | -0.108 | 0.067 | -0.071 | -0.059 | -0.009 | 0.029 | 0.969 | 0.467 | 0.213 | 0.346 | 0.265 | 0.284 | 0.087 | 1 | 0.338 | 0.588 |
| wharves_industry | 0.354 | -0.163 | -0.152 | 0.156 | 0.037 | 0.385 | 0.101 | 0.343 | 0.516 | 0.608 | 0.516 | 0.122 | 0.371 | 0.369 | -0.14 | -0.177 | 0.008 | 0.079 | 0.254 | 0.95 | 0.824 | 0.946 | 0.457 | 0.551 | 0.482 | 0.338 | 1 | 0.873 |
| CI | 0.365 | -0.131 | -0.164 | 0.169 | 0.097 | 0.45 | 0.294 | 0.487 | 0.643 | 0.664 | 0.611 | 0.18 | 0.483 | 0.532 | -0.241 | -0.142 | -0.161 | -0.033 | 0.563 | 0.942 | 0.642 | 0.79 | 0.679 | 0.722 | 0.659 | 0.588 | 0.873 | 1 |
For the regressions, several types of models were considered: linear, quadratic, exponential and logarithmic. Only linear and quadratic models were implemented as there are some bugs with the calculation of the others. The model with the highest \(R^{2}\) is presented on each plot.
OM
Gravel
Sand
Silt
Clay
Arsenic
Cadmium
Chromium
Copper
Iron
Manganese
Mercury
Lead
Zinc
S
N
H
J
CityInf
InduInf
DredColl
DredDump
MoorSite
RainSew
WastSew
CityWha
InduWha
Cumulative Influence
1.2. Species abundances by cumulative influence group
Quitting from lines 301-355 (C2_analyses_B.Rmd) Error in pandoc.table.return(…) : Wrong number of parameters (11 instead of 12) passed: justify De plus : Warning messages: 1: attribute variables are assumed to be spatially constant throughout all geometries 2: attribute variables are assumed to be spatially constant throughout all geometries 3: attribute variables are assumed to be spatially constant throughout all geometries 4: attribute variables are assumed to be spatially constant throughout all geometries 5: attribute variables are assumed to be spatially constant throughout all geometries
2. Hierarchical Modelling of Species Communities
We will use the probabilities and indices of influences calculated in Section 1 here. The aim is to obtain predictive models for the benthic communities, based on the abiotic parameters and the human activities.
HMSC models have been developped in a dedicated script, and the R workspace has been imported here.
First, we initiate the HMSC model with the chosen data, priors and parameters.
Here are the diagnostics to evaluate each model’s quality.
Human activities
Trace plots
Explanatory power
Confidence intervals
Variance partitioning
Habitat parameters
Trace plots
Explanatory power
Confidence intervals
Variance partitioning
All variables
Trace plots
Explanatory power
Confidence intervals
Variance partitioning
Finally, we can predict the values of our parameters within BSI.